Preface
This textbook is aimed at graduate students and upper level undergraduates
in mathematics, engineering, and computer science. The material and the ap-
proach of the text were developed over several years at Auburn University in two
independent courses, Information Theory and Data Compression. Although the
material in the two courses is related, we think it unwise for information theory
to be a prerequisite for data compression, and have written the data compression
section of the text so that it can be read by or presentedtostudents with no prior
knowledge of information theory. There are references in the data compression
part to results and proofs in the information theory part of the text, and those
who are interested may browse over those references, but it is not absolu tely
necessary to do so. In fact, perhaps the best pedagogical order of approach to
these subjects is the reverseoftheapparent logical order: students will come
to information theory curious an d bette rprepared for having seen some of the
definitions and theorems of that subject playing a role in data compression.
Our main aim in the data compression part of the text, as well as in the
course it grew from, is to acquaint the students with a number of significant
lossless compression techniques, and to discuss two lossy compression meth-
ods. Our aim is for the students to emerge competent in and broadly conversant
with a large range of techniques. We have striven for a “practical” style of
presentation: here is what you do and here is what it is good for. Nonethe-
less, proofs are provided, sometimes in the text, sometimes in the exercises, so
that the instructor can have the option of emphasizing the mathematics of data
compression to some degree.
Information theory is of a more theoretical nature than data compression.
It provides a vocabulary and a certain abstraction that can bring the power of
simplification to many different situations. We thought it reasonable to treat it
as a mathematical theory and to present the fundamental definitions and ele-
mentary results of that theory in utter abstraction from the particular problems
of communication through noisy channels, which inspired the theory in the first
place. We bring the theory to bear on noisy channels in Chapters 3 and 4.
The treatment of information theory given here is extremely elementary.
The channels are memoryless and discrete, and the sources are all “zeroth-
order,” one-state sources (although more complicated source models are dis-
cussed in Chapter 7). We feel that this elementary approach is appropriate for
the target audience, and that, by leavingmorecomplicated sources and channels
out of the picture, we more effectively impart the grasp of Information Theory
that we hope our students will take with them.
The exercises range from the routine to somewhat lengthy problems that
introduce additional material or establish more difficult results. An asterisk by
v
© 2003 by CRC Press LLC
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